2 citations · 2 across the 5 of their papers we have counts for
5 papers
Bridging Input Feature Spaces Towards Graph Foundation Models
Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua +2
Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but also in value ranges and di…
DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to…
On the Effectiveness of Random Weights in Graph Neural Networks
Thu Bui, Carola-Bibiane Schönlieb, Bruno Ribeiro +2
Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers.…
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb +3
In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible ac…
Efficient Subgraph GNNs by Learning Effective Selection Policies
Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom +2
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computatio…